SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2018 can be found here.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.
Announcements for external summer programs are listed here.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Data Assimilation and Machine Learning for Climate Models|
|Disciplines:||Environmental Science and Engineering, Applied and Computational Mathematics|
|Mentor:||Tapio Schneider, Frank J. Gilloon Professor, (GPS), email@example.com|
|Mentor URL:||http://climate-dynamics.org (opens in new window)|
Climate change is upon us. Even with mitigation strategies, it will require us to adapt infrastructure such as water reservoirs and flood control measures to a new normal. But what that new normal is remains unclear. Projections of climate and of the risk of climate hazards continue to be marred by unacceptably large uncertainties, and the uncertainties themselves are inadequately quantified, hampering informed decision-making and costing society a chance to adapt proactively and effectively.
Breakthroughs in the accuracy of climate projections are now within reach, thanks to recent advances in the computational and data sciences and in the availability of Earth observations from space and from the ground. Data assimilation and machine learning techniques can be used to harness observational data and data generated computationally in high-resolution simulations to inform models of processes such as clouds and turbulence, which dominate the uncertainties in climate change projections.
In this project, SURF students will contribute to the development of data assimilation and machine learning algorithms and their application in process models of clouds and turbulence. We will use data generated computationally in large-eddy simulations to inform coarse-grained cloud and turbulence models, which we are currently developing for inclusion in climate models. Up to three SURF students will work on various aspects of this development, including the statistical analysis of cloud and turbulence data, testing and development of algorithms for learning from such data, and testing of coarse-grained process models.
Schneider, T., J. Teixeira, C. S. Bretherton, F. Brient, K. G. Pressel, C. Schär, and A. P. Siebesma, 2017: Climate goals and computing the future of clouds. Nature Climate Change, 7, 3-5. http://rdcu.be/ohot
Schneider, T., S. Lan, A. Stuart, and J. Teixeira, 2017: Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations. Geophysical Research Letters, 44, 12,396–12,417. http://climate-dynamics.org/wp-content/uploads/2017/08/Schneider-etal-ESM-2017.pdf
|Student Requirements:||Basic knowledge in statistics and applied mathematics (especially linear algebra) is important. Some background in coding (in any language) is also required.|
This AO can be done under the following programs:
<< Prev Record 42 of 151 Next >> Back To List